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Related Concept Videos

Proteomics01:33

Proteomics

A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
Proteomics is the study of proteomes' function. It involves the large-scale systematic study of the proteome to denote the protein complement expressed by a genome. Scientist Mark Wilkins coined the term proteomics...

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Related Experiment Video

Updated: Jul 14, 2026

Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification
10:37

Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification

Published on: November 15, 2017

Bioimage analysis in deep visual proteomics: Advancing transparency, reproducibility, and FAIR principles.

Devon Siemes1, Angelo Novak1, Stephanie Thiebes1

  • 1Department of Immunodynamics, Institute of Experimental Immunology and Imaging, University Hospital Essen, Essen, Germany.

Journal of Microscopy
|July 13, 2026
PubMed
Summary

We developed an open-source framework to improve the accuracy of laser capture microdissection (LMD) for Deep Visual Proteomics (DVP). This enhances the spatial analysis of cellular proteomic data by standardizing microdissection procedures.

Keywords:
deep visual proteomicsimage registrationlaser capture microdissectionopen-source softwarespatial biology

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Navigating the Mass Spectrometry-Based Proteomic Data Using Free Computational Tools
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Navigating the Mass Spectrometry-Based Proteomic Data Using Free Computational Tools

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Related Experiment Videos

Last Updated: Jul 14, 2026

Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification
10:37

Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification

Published on: November 15, 2017

Navigating the Mass Spectrometry-Based Proteomic Data Using Free Computational Tools
07:01

Navigating the Mass Spectrometry-Based Proteomic Data Using Free Computational Tools

Published on: August 19, 2025

Area of Science:

  • Spatial biology
  • Proteomics
  • Microscopy

Background:

  • Deep Visual Proteomics (DVP) integrates microscopy, computational analysis, and mass spectrometry for spatially resolved molecular profiling.
  • Laser capture microdissection (LMD) is crucial for isolating specific cellular populations in DVP.
  • Current image-to-stage transfer in DVP faces challenges in accuracy and reproducibility.

Purpose of the Study:

  • To present an open-source, semi-automated framework for improving image-to-LMD interoperability in DVP.
  • To enhance the reproducibility and standardization of microdissection for spatial proteomics.

Main Methods:

  • Developed a framework integrating fiducial marker detection and coordinate transformation.
  • Implemented segmentation-driven contour export for precise microdissection.
  • Utilized neutrophil isolation from murine urinary bladder tissue as a model system.

Main Results:

  • Demonstrated robust alignment between computationally defined cellular boundaries and LMD-guided excision.
  • Successfully isolated specific cellular populations with improved accuracy.

Conclusions:

  • The framework enhances interoperability, transparency, and standardization in DVP workflows.
  • Strengthens the integration of microscopy and spatial proteomics for reproducible analysis of immune cells.